A scheduling problem for a novel container transport system: a case of mobile harbor operation schedule

Mobile Harbor (MH) is a movable floating platform with a container handling system on board so that it can load/discharge containers to/from an anchored container ship in the open sea. As with typical quay crane operation, an efficient schedule for its operation is a key to enhancing its operational productivity. A MH operation scheduling problem is to determine a timed sequence of loading/discharging tasks, assignment of MH units to each task, and their docking position, with an objective of minimizing the makespan of a series of incoming container ships. A mixed integer programming model is formulated to formally define the problem. As a practical solution method to the problem, this paper proposes a rule-based algorithm and a random key based genetic algorithm (rkGA). Computational results show that the rkGA method produces a better-quality solution than the rule-based method, while requiring longer computation time.     

Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution Optimisation Problem

<正> Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.     

THE EFFECT OF MALEIC HYDRAZIDE ON THE GROWTH RESPONSE OF PLANTS TO GIBBERELLIC ACID

Maleic hydrazide (MH) and gibberellic acid (GA) were applied alone and in combination at various doses to dwarf and tall varieties of garden pea, and their effect on stem extension measured. Combinations of MH and 3-indolylacetic acid (IAA) were also studied. Stern extension of dwarf peas was accelerated by GA and inhibited by MH. Their effects were not additive, since MH reduced the response to GA at all concentrations of each tested. IAA did not affect stem extension, whether applied alone or in combination with MH. Stem extensions of tall peas was not affected by GA or IAA alone. MH severely inhibited growth and this inhibition was not reduced either by GA or by IAA. At low doses MH broke apical dominance and side branches developed; extension of these was stimulated by GA and IAA and extension of the main axis correspondingly still further reduced. The results show that MH prevents the response to GA of GA-sensitive plants. It is suggested that the rapid growth of tall peas, as compared with that of dwarfs, and their lack of response to GA, are due to a greater capacity to synthesize a 'GA-like hormone'. Growth of tall peas is much more drastically inhibited by MH than that of dwarf peas and the suggestion is made that the inhibition of shoot growth induced by MH is due primarily to blocking the activity of the postulated 'GA-like hormone'.     

Time Varying Optimization for Monitoring Multiple Contaminants under Uncertain Hydrogeology

This study presents a method for identifying cost effective sampling designs for long-term monitoring of remediation of groundwater over multiple monitoring periods under uncertain flow conditions. A contaminant transport model is used to simulate plume migration under many equally likely stochastic hydraulic conductivity fields and provides representative samples of contaminant concentrations. Monitoring costs are minimized under a constraint to meet an acceptable level of error in the estimation of total mass for multiple contaminants simultaneously over many equiprobable realizations of hydraulic conductivity field. A new myopic heuristic algorithm (MS-ER) that combines a new error-reducing search neighborhood is developed to solve the optimization problem. A simulated annealing algorithm using the error-reducing neighborhood (SA-ER) and a genetic algorithm (GA) are also considered for solving the optimization problem. The method is applied to a hypothetical aquifer where enhanced anaerobic bioremediation of four toxic chlorinated ethene species is modeled using a complex contaminant transport model. The MS-ER algorithm consistently performed better in multiple trials of each algorithm when compared to SA-ER and GA. The best design of MS-ER algorithm produced a savings of nearly 25% in project cost over a conservative sampling plan that uses all possible locations and samples.     

Inference of S-system models of gene regulatory networks using immune algorithm

The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses.     

Time Varying Optimization for Monitoring Multiple Contaminants under Uncertain Hydrogeology

This study presents a method for identifying cost effective sampling designs for long-term monitoring of remediation of groundwater over multiple monitoring periods under uncertain flow conditions. A contaminant transport model is used to simulate plume migration under many equally likely stochastic hydraulic conductivity fields and provides representative samples of contaminant concentrations. Monitoring costs are minimized under a constraint to meet an acceptable level of error in the estimation of total mass for multiple contaminants simultaneously over many equiprobable realizations of hydraulic conductivity field. A new myopic heuristic algorithm (MS-ER) that combines a new error-reducing search neighborhood is developed to solve the optimization problem. A simulated annealing algorithm using the error-reducing neighborhood (SA-ER) and a genetic algorithm (GA) are also considered for solving the optimization problem. The method is applied to a hypothetical aquifer where enhanced anaerobic bioremediation of four toxic chlorinated ethene species is modeled using a complex contaminant transport model. The MS-ER algorithm consistently performed better in multiple trials of each algorithm when compared to SA-ER and GA. The best design of MS-ER algorithm produced a savings of nearly 25% in project cost over a conservative sampling plan that uses all possible locations and samples.     

An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance

An evolutionary program, based on a real-code genetic algorithm (GA), is applied to calculate optimal control policies for bioreactors. The GA is used as a nonlinear optimizer in combination with simulation software and constraint handling procedures. A new class of GA-operators is introduced to obtain smooth control trajectories, which leads also to a drastic reduction in computational load. The proposed method is easy to understand and has no restrictions on the model type and structure. The performance and optimal trajectories obtained by the extended GA are compared with those calculated with two common methods: (i) dynamic programming, and (ii) a Hamiltonian based gradient algorithm. The GA proved to be a good and often superior alternative for solving optimal control problems.     

Systematic search for the rate constants that control the exocytotic process from chromaffin cells by a genetic algorithm

We have recently created a kinetic model that reproduces the dynamics of exocytosis with high accuracy. The reconstruction necessitated a search, in a multi-dimensional parameter space, for 37 parameters that described the system, with no assurance that the parameters, which reconstructed the observations, are a unique set. In the present study, a Genetic Algorithm (GA) was used for a thorough search in the unknown parameter space, using a strategy of gradual increase of the complexity of the analyzed input data. Upon systematic incorporation of one to four measurable parameters, used as input signals for the analysis, the constraint set on the GA search imposed the convergence of the free parameters into a single narrow range. The mean values for each adjustable parameter represent a minimum for the fitness function in the multi-dimensional parameter space. The GA search demonstrates that the parameters that control the kinetics of exocytosis are the rate constants of the steps downstream to synaptotagmin binding, and that the equilibrium constant of the binding of calcium to Munc13 controls the calcium-dependent priming process. Thus, the systematic use of the GA creates a link between specific reactions in the process of exocytosis and experimental phenotypes.     

外源脱落酸和马来酰肼对杂交水稻F1穗上种子发芽的抑制效应

杂交稻F1齐穗后21 d(灌浆期)施用ABA 1000mg/L或MH 4000 mg/L,可以抑制穗芽的发生,降低种子活力.ABA处理F1发芽种子使GA1含量下降,淀粉酶活性表达滞后,活性下降,有效发芽期延长;MH处理没有引起F1发芽种子GA1含量与淀粉酶活性下降,但使未发芽种子中GA1与淀粉酶活性明显下降,并丧失发芽能力.因此,ABA对杂交稻F1穗芽的抑制作用可视为"后熟效应",而MH对穗芽的抑制作用可视为"遏制作用".     

Detection of closely linked multiple quantitative trait loci using a genetic algorithm

The existence of a quantitative trait locus (QTL) is usually tested using the likelihood of the quantitative trait on the basis of phenotypic character data plus the recombination fraction between QTL and flanking markers. When doing this, the likelihood is calculated for all possible locations on the linkage map. When multiple QTL are suspected close by, it is impractical to calculate the likelihood for all possible combinations of numbers and locations of QTL. Here, we propose a genetic algorithm (GA) for the heuristic solution of this problem. GA can globally search the optimum by improving the "genotype" with alterations called "recombination" and "mutation." The "genotype" of our GA is the number and location of QTL. The "fitness" is a function based on the likelihood plus Akaike's information criterion (AIC), which helps avoid false-positive QTL. A simulation study comparing the new method with existing QTL mapping packages shows the advantage of the new GA. The GA reliably distinguishes multiple QTL located in a single marker interval.     

A flexible crane scheduling methodology for container terminals

In this paper, we propose a flexible neighbourhood search strategy for quay crane scheduling problems based on the framework of tabu search (TS) algorithm. In the literature, the container workload of a ship is partitioned into a number of fixed jobs to deal with the complexity of the problem. In this paper, we propose flexible jobs which are dynamically changed by TS throughout the search process to eliminate the impact of fixed jobs on the generated schedules. Alternative job sequences are investigated for quay cranes and a new quay crane dispatching policy is developed to generate schedules. Computational experiments conducted with problem instances available in the literature showed that our algorithm is capable of generating quality schedules for quay crane handling operations at reasonable times.     

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.     

A New Cyclic Scheduling Algorithm for Flexible Manufacturing Systems

Flexible manufacturing system control is an NP-hard problem. A cyclic approach has been demonstrated to be adequate for an infinite scheduling problem because of maximal throughput reachability. However, it is not the only optimization criterion in general. In this article we consider the minimization of the work in process (WIP) as an economical and productivity factor. We propose a new cyclic scheduling algorithm giving the maximal throughput (a hard constraint) while minimizing WIP. This algorithm is based on progressive operations placing. A controlled beam search approach has been developed to determine at each step the schedule of the next operations. After presenting the main principles of the algorithm, we compare our approach to several most known cyclic scheduling algorithms using a significant existing example from the literature.     

Conservation Priorities when Species Interact: The Noah's Ark Metaphor Revisited

This note incorporates ecological interactions into the Noah''s Ark problem. In doing so, we arrive at a general model for ranking in situ conservation projects accounting for species interrelations and provide an operational cost-effectiveness method for the selection of best preserving diversity projects under a limited budget constraint.     

Zinc Finger-like Motif Conserved in a Family of RNA Binding Proteins

NP220s compose a family of RNA binding proteins together with matrin 3, one of major proteins of the nuclear matrix. They have repeats of RNA recognition motif (RRM; MH2) homologous to RRM in heterogeneous nuclear RNPs I/L in addition to MH1 and MH3 with unknown function. In search of additional homologous sequences, we found the reported sequence of rat matrin 3 is partially incorrect. Correction of this sequence showed that the NP220 family has a fourth homologous motif with the characteristics of a Cys₂-His₂ zinc finger-like motif. The sequence of this motif is perfectly conserved in human and mouse NP220s despite their 75% overall sequence homology.     

Risk factors for fouling biomass: evidence from small vessels in Australia

Abstract

Invasive non-indigenous species (NIS) are a threat to marine biodiversity and marine reliant industries. Recreational vessels are recognised as an important vector of NIS translocation, particularly domestically. This paper reports on a novel application of multilevel modelling and multiple imputation in order to quantify the relationship between biofouling biomass (wet weight) and the vessel-level characteristics of recreational and fishing vessels. It was found that the number of days since the vessel was last cleaned strongly related to the biofouling biomass, yet differed dependent on vessel type. Similarly, the median number of trips undertaken was related to the biofouling biomass, and varied according to the type of antifouling paint (AF) used. No relationship was found between vessel size and biofouling biomass per sample unit. To reduce the spread of NIS, vessel owners should use an AF paint suitable to their vessel’s operational profile, and follow a maintenance schedule according to the paint manufacturer’s specifications.     

Optimisation of halogenase enzyme activity by application of a genetic algorithm

A genetic algorithm (GA) was applied for the optimisation of an enzyme assay composition respectively the enzyme activity of a recombinantly produced FADH(2)-dependent halogenating enzyme. The examined enzyme belongs to the class of halogenases and is capable to halogenate tryptophan regioselective in position 5. Therefore, the expressed trp-5-halogenase can be an interesting tool in the manufacturing of serotonin precursors. The application of stochastic search strategies (e.g. GAs) is well suited for fast determination of the global optimum in multidimensional search spaces, where statistical approaches or even the popular classical one-factor-at-a-time method often failures by misleading to local optima. The concentrations of six different medium components were optimised and the maximum yield of the halogenated tryptophan could be increased from 3.5 up to 65%.     

An Efficient Wavelet-Based Approximation Method to Gene Propagation Model Arising in Population Biology

In this paper, we have applied an efficient wavelet-based approximation method for solving the Fisher’s type and the fractional Fisher’s type equations arising in biological sciences. To the best of our knowledge, until now there is no rigorous wavelet solution has been addressed for the Fisher’s and fractional Fisher’s equations. The highest derivative in the differential equation is expanded into Legendre series; this approximation is integrated while the boundary conditions are applied using integration constants. With the help of Legendre wavelets operational matrices, the Fisher’s equation and the fractional Fisher’s equation are converted into a system of algebraic equations. Block-pulse functions are used to investigate the Legendre wavelets coefficient vectors of nonlinear terms. The convergence of the proposed methods is proved. Finally, we have given some numerical examples to demonstrate the validity and applicability of the method.     

Competitive spiking and indirect entropy minimization of rate code: efficient search for hidden components.

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. 'Spikes' are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.     

Nucleic acid molecular hybridization as a method for the laboratory diagnosis of influenza: its potentials and prospects

Experiments carried out by the present moment in a number of laboratories indicate that the method of molecular hybridization (MH) has great diagnostic potential. Sufficient methodological mastery of the reaction of radioactively labeled DNA probes with RNA samples applied into a polymer membrane and good supply of commercially available materials make it possible to recommend this method for use in reference laboratories at specialized diagnostic centers. Hybridization should be used in combination with traditional virological and serological tests; the combined use of MH and the enzyme immunoassay for the determination of viral antigens permits the documentation of 90-98% of all cases of influenza A with sufficient rapidity. In the near future DNA probes for the diagnosis of influenza B and C are likely to appear. MH has rather good prospects for the analysis of experimental infection in laboratory animals, as well as for the study of the replication of influenza virus in all cultures. The prospects of the study of the processes of expression of individual genes seem to be particularly attractive. MH may play an important role as a tool for documenting vaccinal reaction, as well as for the study of the action of different chemical preparations in volunteers. And finally, the greatest expectations are linked with the use of MH for the search of inapparent (persistent, latent, etc.) forms of influenza virus infection both in experimental systems and in humans. Optimistic prospects of the studies in this field are based on high sensitivity of this method combined with its equally high specificity. An additional reserve for enhancing sensitivity is also present here due to the amplification of target molecules.